TY  - CHAP
AU  - Hongsheng Dai
ED  - Niansheng Tang
Y1  - 2019-11-13
PY  - 2019
T1  - A Review on the Exact Monte Carlo Simulation
N2  - Due to great applications in various fields, such as social science, biomedicine, genomics, and signal processing, and the improvement of computing ability, Bayesian inference has made substantial developments for analyzing complicated data. This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. It is structured around topics on the impact of the choice of the prior on Bayesian statistics, some advances on Bayesian sampling methods, and Bayesian inference for complicated data including breast cancer data, cloud-based healthcare data, gene network data, and longitudinal data. This volume is designed for statisticians, engineers, doctors, and machine learning researchers.
BT  - Bayesian Inference on Complicated Data
SP  - Ch. 3
UR  - https://doi.org/10.5772/intechopen.88619
DO  - 10.5772/intechopen.88619
SN  - 978-1-83880-386-5
PB  - IntechOpen
CY  - Rijeka
Y2  - 2024-04-26
ER  -